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Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study

Author

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  • Abdulaziz Alhammad

    (Geospatial Sciences, School of Science, RMIT University, Melbourne 3000, Australia)

  • Qian (Chayn) Sun

    (Geospatial Sciences, School of Science, RMIT University, Melbourne 3000, Australia)

  • Yaguang Tao

    (Geospatial Sciences, School of Science, RMIT University, Melbourne 3000, Australia)

Abstract

Many countries have set a goal for a carbon neutral future, and the adoption of solar energy as an alternative energy source to fossil fuel is one of the major measures planned. Yet not all locations are equally suitable for solar energy generation. This is due to uneven solar radiation distribution as well as various environmental factors. A number of studies in the literature have used multicriteria decision analysis (MCDA) to determine the most suitable places to build solar power plants. To the best of our knowledge, no study has addressed the subject of optimal solar plant site identification for the Al-Qassim region, although developing renewable energy in Saudi Arabia has been put on the agenda. This paper developed a spatial MCDA framework catering to the characteristics of the Al-Qassim region. The framework adopts several tools used in Geographic Information Systems (GIS), such as Random Forest (RF) raster classification and model builder. The framework aims to ascertain the ideal sites for solar power plants in the Al-Qassim region in terms of the amount of potential photovoltaic electricity production (PVOUT) that could be produced from solar energy. For that, a combination of GIS and Analytical Hierarchy Process (AHP) techniques were employed to determine five sub-criteria weights (Slope, Global Horizontal Irradiance (GHI), proximity to roads, proximity to residential areas, proximity to powerlines) before performing spatial MCDA. The result showed that ‘the most suitable’ and ‘suitable’ areas for the establishment of solar plants are in the south and southwest of the region, representing about 17.53% of the study area. The ‘unsuitable’ areas account for about 10.17% of the total study area, which is mainly concentrated in the northern part. The rest of the region is further classified into ‘moderate’ and ‘restricted’ areas, which account for 46.42% and 25.88%, respectively. The most suitable area for potential solar energy, yields approximately 1905 Kwh/Kwp in terms of PVOUT. The proposed framework also has the potential to be applied to other regions nationally and internationally. This work contributes a reproducible GIS workflow for a low-cost but accurate adoption of a solar energy plan to achieve sustainable development goals.

Suggested Citation

  • Abdulaziz Alhammad & Qian (Chayn) Sun & Yaguang Tao, 2022. "Optimal Solar Plant Site Identification Using GIS and Remote Sensing: Framework and Case Study," Energies, MDPI, vol. 15(1), pages 1-21, January.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:1:p:312-:d:716715
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    References listed on IDEAS

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    1. Amro M Elshurafa & Abdel Rahman Muhsen, 2019. "The Upper Limit of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Sustainability, MDPI, vol. 11(16), pages 1-20, August.
    2. Al Garni, Hassan Z. & Awasthi, Anjali, 2017. "Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia," Applied Energy, Elsevier, vol. 206(C), pages 1225-1240.
    3. Abdulsalam S. Alghamdi, 2019. "Potential for Rooftop-Mounted PV Power Generation to Meet Domestic Electrical Demand in Saudi Arabia: Case Study of a Villa in Jeddah," Energies, MDPI, vol. 12(23), pages 1-29, November.
    4. Abdelrahman Muhsen & Amro Elshurafa, 2019. "The Potential of Distributed Solar PV Capacity in Riyadh: A GIS-Assisted Study," Discussion Papers ks--2019-dp74, King Abdullah Petroleum Studies and Research Center.
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    2. Abdellah Menou & Risto Lahdelma & Pekka Salminen, 2022. "Multicriteria Decision Aiding for Planning Renewable Power Production at Moroccan Airports," Energies, MDPI, vol. 15(14), pages 1-20, July.
    3. Mao, Hongzhi & Chen, Xie & Luo, Yongqiang & Deng, Jie & Tian, Zhiyong & Yu, Jinghua & Xiao, Yimin & Fan, Jianhua, 2023. "Advances and prospects on estimating solar photovoltaic installation capacity and potential based on satellite and aerial images," Renewable and Sustainable Energy Reviews, Elsevier, vol. 179(C).
    4. Jerome G. Gacu & Junrey D. Garcia & Eddie G. Fetalvero & Merian P. Catajay-Mani & Cris Edward F. Monjardin, 2023. "Suitability Analysis Using GIS-Based Analytic Hierarchy Process (AHP) for Solar Power Exploration," Energies, MDPI, vol. 16(18), pages 1-28, September.
    5. Sumit Kalyan & Qian (Chayn) Sun, 2022. "Interrogating the Installation Gap and Potential of Solar Photovoltaic Systems Using GIS and Deep Learning," Energies, MDPI, vol. 15(10), pages 1-21, May.
    6. Kamali Saraji, Mahyar & Aliasgari, Elahe & Streimikiene, Dalia, 2023. "Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    7. Miyake, Saori & Teske, Sven & Rispler, Jonathan & Feenstra, Maartje, 2024. "Solar and wind energy potential under land-resource constrained conditions in the Group of Twenty (G20)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    8. Maimó-Far, Aina & Homar, Víctor & Tantet, Alexis & Drobinski, Philippe, 2024. "The trade-off between socio-environmental awareness and renewable penetration targets in energy transition roadmaps," Applied Energy, Elsevier, vol. 355(C).
    9. Fida Ali & Adul Bennui & Shahariar Chowdhury & Kuaanan Techato, 2022. "Suitable Site Selection for Solar-Based Green Hydrogen in Southern Thailand Using GIS-MCDM Approach," Sustainability, MDPI, vol. 14(11), pages 1-22, May.
    10. Al-Awadhi, Talal & Al Ramimi, Waleed & Al Jabri, Khalifa & Abulibdeh, Ammar, 2025. "Solar farms suitability analysis using GIS-Based Analytic Hierarchy Process (AHP) multi-criteria: A case study of Al Duqm – Oman," Renewable Energy, Elsevier, vol. 241(C).

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